Selection of the most useful subset of genes for gene expression-based classification

@article{Paul2004SelectionOT,
  title={Selection of the most useful subset of genes for gene expression-based classification},
  author={Topon Kumar Paul and Hitoshi Iba},
  journal={Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)},
  year={2004},
  volume={2},
  pages={2076-2083 Vol.2}
}
Recently, there has been a growing interest in classification of patient samples based on gene expressions. Here the classification task is made more difficult by the noisy nature of the data, and by the overwhelming number of genes relative to the number of available training samples in the data set. Moreover, many of these genes are irrelevant for classification and have negative effect on the accuracy and on the required learning time for the classifier. We propose a new evolutionary… CONTINUE READING
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